Named entity recognition (NER) is an important task in NLP and it helps many other NLP applications such as information retrieval, question answering, and relation extraction. As we discussed before, NER is the first step for pipeline relation extraction models. The goal of the NER task is to identify named entities such as person names, location names, and organization names in a text. A sequence tagging approach, called the `BIESO' tagging scheme, is used to identify the entities in a text. In this tagging scheme, every token of a text is assigned a tag out of the 5 tags `B', `I', `E', `S', and `O'. `B' refers to the beginning of an entity, `I' refers to the inside of an entity, and `E' refers to the end of an entity. `S' is used to tag single-token entities and `O' is used to tag the non-entity tokens. An entity type is appended as the suffix to a `B/I/E/S/O' tag to indicate the type of the entity such as PERSON, LOCATION, ORGANIZATION, GEOPOLITICAL ENTITY, etc. Several named entity recognition shared tasks were organized with accompanying datasets such as MUC [1]}, CoNLL 2002 [2]}, CoNLL 2003 [3]}, and ACE04 [4]}.
